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Moondream MCP Server

Moondream MCP Server

by NightTrek
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript

analyze_image

Analyze an image with the Moondream model to generate captions, detect specified objects, or answer visual questions using an image path and prompt.

Instructions

Analyze an image using the Moondream model

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "image_path": "<image_path>",
      "prompt": "<prompt>"
    },
    "name": "analyze_image"
  }
}

Input Schema

NameRequiredDescriptionDefault
promptYesCommand to analyze the image. Use 'generate caption' for image captioning, 'detect: [object]' for object detection, or any question for image querying.
image_pathYesPath to the image file to analyze

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It names the model but does not disclose key behavior such as whether the operation is read-only, how the local image path is accessed, what permissions are needed, or how output varies by prompt.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

It is a single front-loaded sentence with no wasted words. The structure is clean, though the brevity leaves important behavioral context unstated.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations and no output schema, the one-line description is not complete enough. The return shape and output differences between captioning, detection, and querying are left entirely implicit.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with both parameters fully documented, including prompt modes for captioning, detection, and querying. The description adds no parameter-specific meaning beyond that schema, so the baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource ('Analyze an image') and names the underlying model ('Moondream'), so the basic purpose is clear. It does not explicitly distinguish this from the sibling 'analyze_webpage', which keeps it from a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance on when to use this tool versus alternatives, nor when not to use it. It also does not mention choosing between captioning, detection, and querying, even though the prompt schema does.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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